class_docstr: Post-processing methods for ABCNet.
  
  Args:
      num_classes (int): Number of classes.
      use_sigmoid_cls (bool): Whether to use sigmoid for classification.
      strides (tuple): Strides of each feature map.
      norm_by_strides (bool): Whether to normalize the regression targets by
          the strides.
      bbox_coder (dict): Config dict for bbox coder.
      text_repr_type (str): Text representation type, 'poly' or 'quad'.
      with_bezier (bool): Whether to use bezier curve for text detection.
      train_cfg (dict): Config dict for training.
      test_cfg (dict): Config dict for testing.
docstr: Get text instance predictions of one image.
  
  Args:
      pred_result (tuple(Tensor)): Prediction results of an image.
      data_sample (TextDetDataSample): Datasample of an image.
      **kwargs: Other parameters. Configurable via ``__init__.train_cfg``
          and ``__init__.test_cfg``.
  
  Returns:
      TextDetDataSample: A new DataSample with predictions filled in.
      The polygon/bbox results are usually saved in
      ``TextDetDataSample.pred_instances.polygons`` or
      ``TextDetDataSample.pred_instances.bboxes``. The confidence scores
      are saved in ``TextDetDataSample.pred_instances.scores``.
docstr_tok: ["Get","text","instance","predictions","of","one","image",".","Args",":","pred_result","(","tuple","(","Tensor",")",")",":","Prediction","results","of","an","image",".","data_sample","(","TextDetDataSample",")",":","Datasample","of","an","image",".","*","*","kwargs",":","Other","parameters",".","Configurable","via","``","__init__.train_cfg","``","and","``","__init__.test_cfg","``",".","Returns",":","TextDetDataSample",":","A","new","DataSample","with","predictions","filled","in",".","The","polygon\/bbox","results","are","usually","saved","in","``","TextDetDataSample.pred_instances.polygons","``","or","``","TextDetDataSample.pred_instances.bboxes","``",".","The","confidence","scores","are","saved","in","``","TextDetDataSample.pred_instances.scores","``","."]
code: def get_text_instances(self, data_sample, **kwargs):
      """Get text instance predictions of one image.
  
      Args:
          pred_result (tuple(Tensor)): Prediction results of an image.
          data_sample (TextDetDataSample): Datasample of an image.
          **kwargs: Other parameters. Configurable via ``__init__.train_cfg``
              and ``__init__.test_cfg``.
  
      Returns:
          TextDetDataSample: A new DataSample with predictions filled in.
          The polygon/bbox results are usually saved in
          ``TextDetDataSample.pred_instances.polygons`` or
          ``TextDetDataSample.pred_instances.bboxes``. The confidence scores
          are saved in ``TextDetDataSample.pred_instances.scores``.
      """
      data_sample = data_sample.cpu().numpy()
      pred_instances = data_sample.pred_instances
      data_sample.pred_instances.polygons = list(
          map(bezier2poly, pred_instances.beziers)
      )
      return data_sample
  
file_path: mmocr/projects/ABCNet/abcnet/model/abcnet_postprocessor.py
imports: from mmocr.models.textdet.postprocessors.base import BaseTextDetPostProcessor
  from mmocr.registry import MODELS
  from ..utils import bezier2poly
label_desc: Use image node_id 5 for calling the ABCNetPostprocessor obj's underlying member method code with example usage: obj.get_text_instances(data_sample) and returns: data_sample